An individual's expotype, particularly cardiovascular, metabolic, and lifestyle factors, significantly predicted grey matter health as measured by the Brain Age Gap (r = 0.23, p = 0.002).
Observational (n=39,390)
Yes
Can exposome-wide variables predict brain health (Brain Age Gap) in aging populations?
Cardiovascular, metabolic, and lifestyle factors are major predictors of brain aging, highlighting the importance of early cardiovascular prevention to promote life-long brain health.
Effect estimate: r = 0.23
p-value: p=0.002
Abstract Promoting brain health is vital for well-being and reducing healthcare burdens. Brain health as measured with the Brain Age Gap (BAG) - the difference between chronological and predicted brain age- relates to many factors. However, a holistic view, integrating the range of factors an individual brain is exposed to, is missing for understanding how the exposome shapes brain health. After computing BAG as an indicator of grey matter (GM) health, we predicted it using machine learning based on 261 exposome variables (spanning biomedical, environmental, lifestyle, socio-affective, and early life domains) in UK Biobank participants. Exposome data can predict GM health with factors pertaining to cardiovascular and bone health, along with alcohol and smoking, nutrition and diabetes showing greater contribution to the prediction. In such domains, life period and duration of exposure appeared crucial. These findings call for early prevention in cardiovascular and metabolic health to promote life-long brain health.
Mahdipour et al. (Fri,) conducted a observational in Brain aging (n=39,390). Exposome variables (Expotype) was evaluated on Prediction of grey matter health (Brain Age Gap) from the expotype (r = 0.23, p=0.002). An individual's expotype, particularly cardiovascular, metabolic, and lifestyle factors, significantly predicted grey matter health as measured by the Brain Age Gap (r = 0.23, p = 0.002).